Abstract
This paper falls in the field of playing analytics. It deals with an empirical work dedicated to explore the potential of data sonification (i.e. the conversion of data into sound that reflects their objective properties or relations). Data sonification is proposed as an alternative to data visualization. We applied data sonification for the analysis of gameplays and players’ strategies during a session dedicated to game-based learning. The data of our study (digital traces) was collected from 200 pre-service teachers who played Tamagocours, an online collaborative multiplayer game dedicated to learn the rules (i.e. copyright) that comply with the policies for the use of digital resources in an educational context. For one typical individual (parangon) for each of the 5 categories of players, the collected digital traces were converted into an audio format so that the actions that they performed become listenable. A specific software, SOnification of DAta for Learning Analytics (SODA4LA), was developed for this purpose. The first results show that different features of the data can be recognized from data listening. These results also enable for the identification of different parameters that should be taken into account for the sonification of diachronic data. We consider that this study open new perspectives for playing analytics. Thus we advocate for new research aiming at exploring the potential of data sonification for the analysis of complex and diachronic datasets in the field of educational sciences.
Keywords
- Sonification
- Playing analytics
- Data visualisation
- Game-based learning
- Learning Analytics
This is a preview of subscription content, access via your institution.
Buying options



References
Avanzo, S., Barbera, R., De Mattia, F., La Rocca, G., Sorrentino, M., Vicinanza, D.: Data sonification of volcano seismograms and sound/timbre reconstruction of ancient musical instruments with grid infrastructures. Procedia Comput. Sci. 1(1), 397–406 (2010)
Berthold, M.R., et al.: KNIME-the konstanz information miner: version 2.0 and beyond. ACM SIGKDD Explor. Newsl. 11(1), 26–31 (2009)
Bregman, A.S.: Auditory Scene Analysis: The Perceptual Organization of Sound. The MIT Press, Cambridge (1990)
Casado, R., Guin, N., Champin, P.-A., Lefevre, M.: kTBS4LA: une plateforme d’analyse de traces fondée sur une modélisation sémantique des traces. In: Méthodologies et outils pour le recueil, l’analyse et la visualisation des traces d’interaction - ORPHEE-RDV, Font-Romeu, France, January 2017 (2017)
Design-Based Research Collective: Design-based research: an emerging paradigm for educational inquiry. Educ. Res. 32(1), 5–8 (2003)
Demšar, J., et al.: Orange: data mining toolbox in python. J. Mach. Learn. Res. 14(1), 2349–2353 (2013)
Handel, S.: Listening: An Introduction to the Perception of Auditory Events. The MIT Press, Cambridge (1993)
Hermann, T., Hunt, A., Neuhoff, J.G.: The Sonification Handbook. Logos Verlag, Berlin (2011)
Holmes, G., Donkin, A., Witten, I.H.: WEKA: a machine learning workbench, pp. 357–361 (1994). https://doi.org/10.1109/ANZIIS.1994.396988
Kramer, G., et al.: The sonification report: status of the field and research agenda. Report prepared for the national science foundation by members of the international community for auditory display. International Community for Auditory Display (ICAD), Santa Fe, NM (1999)
Sanchez, E., Mandran, N.: Exploring competition and collaboration behaviors in game-based learning with playing analytics. In: Lavoué, É., Drachsler, H., Verbert, K., Broisin, J., Pérez-Sanagustín, M. (eds.) EC-TEL 2017. LNCS, vol. 10474, pp. 467–472. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66610-5_44
Sanchez, E., Martinez-Emin, V., Mandran, N.: Jeu-game, jeu-play, vers une modélisation du jeu. Une étude empirique à partir des traces numériques d’interaction du jeu Tamagocours. Sciences et Technologies de l’Information et de la Communication pour l’Éducation et la Formation 22(1), 9–44 (2015)
Sanchez, E., Monod-Ansaldi, R., Vincent, C., Safadi-Katouzian, S.: A praxeological perspective for the design and implementation of a digital role-play game. Educ. Inf. Technol. 22(6), 2805–2824 (2017)
Speeth, S.D.: Seismometer sounds. J. Acoust. Soc. Am. 33(7), 909–916 (1961)
RStudio Team, et al.: Rstudio: Integrated Development for R. RStudio, Inc., Boston, 42:14 (2015). http://www.rstudio.com
Worrall, D.: Using sound to identify correlations in market data. In: Ystad, S., Aramaki, M., Kronland-Martinet, R., Jensen, K. (eds.) CMMR/ICAD-2009. LNCS, vol. 5954, pp. 202–218. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12439-6_11
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Supplementary Material
Supplementary Material
Scores, audio files, and midi files are available here: https://drive.google.com/open?id=1SLMg071XR7Mn14iIV7PdlkstHXGejRa4.
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sanchez, E., Sanchez, T. (2019). Let’s Listen to the Data: Sonification for Learning Analytics. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-33232-7_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33231-0
Online ISBN: 978-3-030-33232-7
eBook Packages: Computer ScienceComputer Science (R0)